Automated Intelligence-Requiring (AI) Task

An Automated Intelligence-Requiring (AI) Task is a information-processing task (data-driven) that is an intelligence-requiring task and an automated task.

• Context:
  • Inputs:
    • Model Input: training data, inference data, model parameters
    • System Input: hardware resources, compute capacity, memory allocation
  • Outputs:
    • Model Output: AI predictions, AI generated content, model performance metrics
    • System Output: resource utilization, execution time, system performance metrics
  • AI Task Performance Measures:
    • Model Quality Metrics: prediction accuracy, generation quality, model reliability
    • System Performance Metrics: throughput, latency, resource efficiency
  • ...
  • It can (often) be part of an AI-Requiring Process.
  • It can (often) be referenced AI-Requiring Process Model.
  • ...
  • It can range from being a Domain-Specific AI Task to being an AI-Complete Task, depending on its scope.
  • It can range from being a Simple AI Task to being a Complex AI Task, depending on its computational requirements.
  • It can range from being a Single-Modal AI Task to being a Multi-Modal AI Task, depending on its input type.
  • It can range from being a Supervised AI Task to being an Unsupervised AI Task, depending on its learning approach.
  • It can range from being a Batch AI Task to being an Real-Time AI Task, depending on its ....
  • It can range from being a Offline AI Task to being an Oneline AI Task, depending on its ....
  • It can range from being a AI System Benchmarking Task to being an Real-Word AI System Assessment Task.
  • It can range from being a Non-Interactive AI Task to being an Interactive AI Task, depending on its ....
  • It can range from being a Rule-Based AI Task to being a Learning-Based AI Task, depending on its implementation method.
  • It can range from being a Single-Metric AI Task to being a Multi-Metric AI Task, depending on its evaluation criteria.
  • It can range from being a Deterministic AI Task to being a Probabilistic AI Task, depending on its output certainty.
  • It can range from being a Single-Stage AI Task to being a Multi-Stage AI Task, depending on its processing pipeline.
  • It can range from being an Automated Learning Task to being an Automated Inference Task, depending on its task purpose.
  • It can range from being a Single-Model AI Task to being a Multi-Model AI Task, depending on its model requirements.
  • It can range from being a Model-Training Task to being a Model-Inference Task, depending on its execution phase.
  • It can range from being a Resource-Light AI Task to being a Resource-Intensive AI Task, depending on its system demands.
  • It can range from being a Single-System Task to being a Distributed-System Task, depending on its execution architecture.
  • It can range from being a Model-Centric Task to being a System-Centric Task, depending on its performance priority.
  • ...
  • It can be solved by an AI System (that implements an AI algorithm).
  • It can require input validation before processing.
  • It can produce verifiable output for evaluation.
  • It can integrate with other AI tasks in a workflow.
  • ...
• Examples:
  • Automated Learning Tasks, such as:
    • Automated Supervised Learning Tasks (automated classification, automated regression)
    • Automated Unsupervised Learning Tasks (automated clustering, automated anomaly detection)
    • Automated Reinforcement Learning Tasks (automated game playing, automated robot control)
    • Automated Transfer Learning Tasks (automated domain adaptation, automated knowledge transfer)
    • Automated Meta-Learning Tasks (automated hyperparameter optimization, automated architecture search)
  • Automated Reasoning Tasks, such as:
    • Automated Planning Tasks (automated route planning, automated resource allocation)
    • Automated Decision Making Tasks (automated trading, automated content moderation)
    • Automated Problem Solving Tasks (automated theorem proving, automated puzzle solving)
    • Automated Logic Tasks (automated inference, automated validation)
    • Automated Strategy Tasks (automated game strategy, automated negotiation)
  • Automated Language Processing Tasks, such as:
    • Automated Text Analysis Tasks (automated sentiment analysis, automated topic modeling, automated intent recognition)
    • Automated Language Generation Tasks (automated story writing, automated code generation, automated report generation)
    • Automated Language Translation Tasks (automated text translation, automated speech translation, automated cross-lingual summarization)
    • Automated Dialog Tasks (automated chat response, automated interview, automated customer support)
    • Automated Document Tasks (automated document classification, automated information extraction, automated document summarization)
  • Automated Perception Tasks, such as:
    • Automated Visual Recognition Tasks (automated face detection, automated object tracking, automated scene understanding)
    • Automated Audio Processing Tasks (automated speech recognition, automated music analysis, automated sound event detection)
    • Automated Multi-Modal Analysis Tasks (automated video captioning, automated audio-visual synchronization, automated cross-modal retrieval)
    • Automated Sensor Processing Tasks (automated motion detection, automated gesture recognition, automated biometric analysis)
  • Automated Creative Tasks, such as:
    • Automated Art Generation Tasks (automated image generation, automated music composition, automated poetry writing)
    • Automated Design Tasks (automated logo design, automated layout generation, automated UI design)
    • Automated Content Creation Tasks (automated video editing, automated animation, automated game content generation)
  • Automated Optimization Tasks, such as:
    • Automated Resource Optimization Tasks (automated scheduling, automated load balancing)
    • Automated Performance Optimization Tasks (automated code optimization, automated system tuning)
    • Automated Process Optimization Tasks (automated workflow optimization, automated supply chain optimization)
  • Automated Control Tasks, such as:
    • Automated Navigation Tasks (automated path planning, automated obstacle avoidance)
    • Automated System Control Tasks (automated temperature control, automated power management)
    • Automated Robot Control Tasks (automated manipulation, automated assembly)
  • Automated Analysis Tasks, such as:
    • Automated Data Analysis Tasks (automated pattern discovery, automated trend analysis)
    • Automated Scientific Analysis Tasks (automated experiment analysis, automated hypothesis generation)
    • Automated Business Analysis Tasks (automated market analysis, automated risk assessment)
  • Automated Security Tasks, such as:
    • Automated Threat Detection Tasks (automated malware detection, automated fraud detection)
    • Automated Security Assessment Tasks (automated vulnerability scanning, automated penetration testing)
    • Automated Security Response Tasks (automated incident response, automated threat mitigation)
  • Automated Monitoring Tasks, such as:
    • Automated System Monitoring Tasks (automated performance monitoring, automated health monitoring)
    • Automated Environment Monitoring Tasks (automated weather monitoring, automated pollution monitoring)
    • Automated Process Monitoring Tasks (automated quality monitoring, automated safety monitoring)
  • Domain-Specific AI Tasks, such as:
    • Medical AI Tasks (automated diagnosis, medical image analysis, drug interaction prediction)
    • Financial AI Tasks (automated trading, fraud detection, risk assessment)
    • Legal AI Tasks (document classification, case law analysis, contract review)
    • Manufacturing AI Tasks (quality control, predictive maintenance, process optimization)
    • Agricultural AI Tasks (crop disease detection, yield prediction, irrigation optimization)
    • Educational AI Tasks (student performance analysis, automated grading, learning path optimization)
    • Weather AI Tasks (weather forecasting, climate modeling, extreme event prediction)
  • Domain-Flexible AI Tasks, such as:
    • Cross-Domain Learning Tasks (transfer learning, multi-domain adaptation, knowledge transfer)
    • General Purpose Analysis Tasks (pattern recognition, anomaly detection, trend analysis)
    • Universal Language Tasks (language translation, text summarization, content generation)
  • Domain-Independent AI Tasks, such as:
    • Generic Reasoning Tasks (logical inference, causal reasoning, abstract thinking)
    • General Problem Solving Tasks (strategy formulation, optimization, decision making)
    • Universal Learning Tasks (concept learning, skill acquisition, knowledge integration)
  • AI-Hard Tasks, such as:
    • Natural Language Understanding Tasks (context comprehension, implicit meaning extraction, pragmatic understanding)
    • Common Sense Reasoning Tasks (everyday logic, human-like inference, situational understanding)
    • General Knowledge Tasks (knowledge integration, cross-domain reasoning, novel situation handling)
  • AI-Complete Tasks, such as:
    • Human-Level Communication Tasks (natural conversation, emotional understanding, contextual interaction)
    • General Intelligence Tasks (abstract reasoning, creative problem solving, adaptable learning)
    • Consciousness Simulation Tasks (self-awareness, consciousness, emotional intelligence)
    • General World Understanding Tasks (scene comprehension, physical interaction understanding, social dynamics understanding)
    • Open-Ended Learning Tasks (continuous learning, knowledge accumulation, skill transfer)
  • ...
• Counter-Example(s):
  • Human Intelligence Tasks, which require direct human cognition.
  • Manual Processing Tasks, which lack automation.
• See: Conversational Task, Simulation Task, Machine Learning Task, AI System Capability.

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References

2024-11-16

• Perplexity.ai
  • Question: Can you explain the main categories and types of tasks that modern AI systems can perform? Please include both basic and complex tasks, ranging from specific domain applications to general AI challenges. Break down how these tasks are classified, their characteristics, and real-world applications. Cover language processing, perception, reasoning, and learning tasks, and explain how they vary in complexity and automation levels. Also include how these tasks are integrated into practical systems and workflows.
  • Answer: Understanding and organizing the diverse range of capabilities and tasks that modern AI systems can perform requires a structured approach to classification and assessment.
    • Core Task Categories:
      • Language Processing Tasks:
        • Text-to-Text Translation
        • Natural Language Understanding
        • Text Generation and Summarization
        • Speech Recognition and Synthesis
        • Sentiment Analysis
      • Perception Tasks:
        • Computer Vision
        • Image Recognition and Classification
        • Object Detection
        • Scene Understanding
        • Audio Processing
      • Reasoning Tasks:
        • Automated Planning
        • Logic-based Problem Solving
        • Decision Making
        • Knowledge Representation
        • Inference Generation
      • Learning Tasks:
        • Pattern Recognition
        • Classification
        • Clustering
        • Prediction/Forecasting
        • Reinforcement Learning
    • Task Complexity Levels:
      • Domain-Specific Tasks:
        • Focused on particular industries with defined boundaries
        • Examples: Medical diagnosis, financial trading, weather forecasting
      • AI-Complete Tasks:
        • Require human-level intelligence
        • Complex, multi-faceted problems
        • Examples: Full natural language understanding, general problem solving
    • Task Characteristics:
      • By Automation Level:
        • Fully Automated
        • Semi-Automated (Human-in-the-Loop)
        • Hybrid Systems
      • By Input/Output Type:
        • Single-Modal Tasks
        • Multi-Modal Tasks
        • Cross-Modal Tasks
    • Real-World Applications:
      • Industrial Applications:
        • Quality Control
        • Process Optimization
        • Predictive Maintenance
      • Service Applications:
        • Customer Service Chatbots
        • Virtual Assistants
        • Recommendation Systems
      • Specialized Applications:
        • Autonomous Vehicle Navigation
        • Robotic Process Automation
        • Scientific Research Analysis
    • Task Integration:
      • System Integration:
        • API-based Integration
        • Microservices Architecture
        • Cloud-based Services
      • Workflow Integration:
        • Business Process Automation
        • Task Orchestration
        • Pipeline Management
    • Solution Design Benefits:
      • Enables effective AI solution design
      • Supports appropriate algorithm selection
      • Facilitates resource allocation planning
      • Helps set realistic task completion expectations

2017

• (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/artificial_intelligence Retrieved:2017-8-1.
  • Artificial intelligence (AI) is intelligence exhibited by machines. In computer science, the field of AI research defines itself as the study of “intelligent agents": any device that perceives its environment and takes actions that maximize its chance of success at some goal. Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving". As machines become increasingly capable, mental facilities once thought to require intelligence are removed from the definition. For instance, optical character recognition is no longer perceived as an example of "artificial intelligence", having become a routine technology. Capabilities currently classified as AI include successfully understanding human speech,competing at a high level in strategic game systems (such as chess and Go), autonomous cars, intelligent routing in content delivery networks, military simulations, and interpreting complex data. AI research is divided into subfields that focus on specific problems, approaches, the use of a particular tool, or towards satisfying particular applications. The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects. General intelligence is among the field's long-term goals.

2010a

• The New Oxford American Dictionary, Third Edition
  • QUOTE: … computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

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